Techniques and Methods of Sampling

Geography, dealing with the man and environment relationship, is essentially a social science. One of the main problems that geogra­phers meet in their pursuit of research is the abundance of data.

As a matter of fact, in the recent decades, an ‘explosion of data’ has taken place in every sphere of life, providing an enormous source of valu­able information in the form of numerical facts for the quantification of socioeconomic problems in space and time.

The increased quan­tity of data, though useful for the formulation of hypotheses and their testing, has created the problems of data processing, its plotting on maps and analysis. The task of researchers has, thereby, been ren­dered arduous, costly and time consuming.

Almost all the branches of geography, e.g., geomorphology, cli­matology, oceanography, pedology, demography, economic, agricul­tural and industrial geography, urban and rural land use planning, transport, urban settlement, electoral and medical geography have all turned to more precise numerical data in their attempts to render a more realistic and objective assessment of geographical phenomena.

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Moreover, now geographers are increasingly cooperating with scien­tists of other disciplines. Application of sound and sophisticated sta­tistical techniques to geographical data has, therefore, become essen­tial.

‘Sampling’ is a useful technique for the processing of data. It is frequently used by geographers in their studies. Sampling of data in itself is a tedious job which requires utmost care on the part of re­searcher to arrive at reliable results.

The essence of sampling lies in the fact that a large number of items, individuals, or locations (statis­tical population) may, within specified limits of statistical prob­ability, be presented by a smaller group of items (a sample) selected from the larger group (a parent population).

Out of the huge popula­tion, if a limited selection of items or cases is made, it is called a ‘sample’. The limited ‘sample’ is generally sufficient for making a generalization about the whole population. In many cases, the numbers of individuals in the population, e.g., the average yield of all the plots of an agricultural region or the pebbles on a sea-beach are so numerous that measuring of all of them would be almost impossible from a practical point of view.

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But, if through sampling, a limited se­lection of fields for the measurement of yields would enable the ob­server to obtain the average yield of the fields in the entire region, similarly, a limited selection of pebbles on the sea-beach will be suf­ficient for making a generalization about the pebbles on that coast.

Sampling, thus, represents a more efficient use of our energy, still al­lowing us to make reliable statements about the whole population. Public opinion polls announce how a nation intends to vote, or analyze people’s attitudes on current issues, but their conclusions are ob­tained from a sample consisting of a few hundred questionnaires, rather than by consulting everyone in the country. Complete enu­meration of population in most of the cases is almost impracticable.

An appropriate sampling in geographical research is highly desirable as it saves time, efforts and cost appreciably and gives reliable results which can be used for the purpose of generalization and forecasting. The problem of choosing the right size of sample, however, is a little more complicated.

The simplest rule is that the larger the size of sample, the more likely it is to give a reliable picture of the parent population. As a further rough guide, it can be said that the size of sample should be at least 5 per cent to 15 per cent of the total for sat­isfactory results. The decisions on defining parent population and choosing the best sampling method, however, depend to a large ex­tent on commonsense.

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Some of the commonly known and frequently used methods of sampling are: random sampling, purposive sampling, systematic sampling, stratified sampling and multistage sampling.

1. Random Sampling:

In the random sampling, the sample units are selected at random. Once the ‘parent population’ has been defined, each item in that population has an equal chance of being included in any sample. In this method, proper care has to be taken to ensure that samples are se­lected at random. Many a time a truly random choice may not be fea­sible.

The investigator should, however, pursue the ideal of random selection as closely as possible. The use of lottery is the simplest method of such sampling. Fairly good samples can also be taken by the use of random sampling numbers given in Table 6.2.

The sampling with the help of random sampling table may be il­lustrated by citing an example. Suppose for the study of agricultural land use of a region having 400 villages, only 15 villages are to be se­lected at random. To begin with the villages will be serially numbered, e.g., 1, 2,3,4,5…….. 400.

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After arranging the villages in a serial order, a page (table) of the random sampling series will be taken. Starting with any figure of that page, the digits occurring in succession (either in rows or in columns) will be written down in blocks of three to give three digit numbers. The number 001 and 002 may be taken to correspond to village 1 and village 2 respectively and finally 400 will correspond to village 400.

All the three digit numbers greater than 400 and also 000 will be ignored. If a number occurring earlier is repeated, a new digit number will be taken until 15 different three digit numbers (none of which should be 000 or greater than 400) are obtained. The following example illustrated with the help of Table 6.3 would make the point more-clear.

The Table 6.2 is given in the values of tens and ones. First, these figures are arranged in blocks of three to give three numbers, as the total number of villages of the area under study goes up to three dig­its (400). Then, all figures which are within 400 are selected, ignor­ing the figures which exceed 400 and also 000. According to the said technique, the 15 sample villages, taken with the help of Table 6.2, will be as follows: 201, 221, 162, 45, 327, 36, 174, 157, 291, 47, 239, 09, 39, 42, and 122. Arranged in a serial order the villages chosen as samples will be 9, 36, 39, 42, 45, 47, 122, 157, 162, 174, 201, 221, 239, and 291,327.

Again, if the full list was made up of 10000 villages, the first four columns of the random sampling table would be used, 0000 rep­resenting 10000. For a selection of 15 villages in this case, the first sample would be the one numbered 2017 (see Table 6.2) on the full list, the next would be number 7449 and so on, until the full list of 15 villages is selected.

In this case, the figures exceeding 10000 and 0000 shall be ignored while making the selection. The random sam­pling table facilitates the work of researchers. The main advantage of the random sampling technique lies in the fact that it is unbiased, more objective and representative of the whole book of data.

2. Purposive Sampling:

In the purposive sampling technique, the samples are selected with a definite purpose in view. For example, if the standard of nutrition of the rural population of a region or country, having vegetarian food habits, is to be determined, only the vegetarian food eating families will be taken as the samples for study.

Similarly, if the change in the standard of living of the agricultural labourers and farmers of a com­ponent areal unit during a specific period of time is to be studied, the samples will be taken from the respective categories, ignoring the rest of the population. This technique of sampling suffers from the drawback of favouritism and fails to give a representative sample of the population.

3. Systematic Sampling:

In this method, a regular pattern of selection is made instead of choosing each individual separately. This method is also known as quasi-random. For instance, if a study of crop combination is to be made in 2000 villages of an areal unit and 20 sample villages are to be selected, the villages should be given a serial order, starting from 1 to 2000.

After arranging the villages serially, every hundredth vil­lage of the list is chosen. The required sample villages will be reached quickly. If used sensibly, systematic sampling can often be more convenient than true random sampling and can be equally ef­fective. This method, though helpful in making quick and effective sampling, however, suffers from the setback of subjectivity as each village of the area does not have an equal chance of being included in the sample.

4. Stratified Sampling:

When the population is heterogeneous with respect to variables un­der enquiry and can be divided into relatively homogeneous groups and subgroups, a stratified sampling technique can be adopted. This type of sampling is mostly applied when there are significant groups of known size within the ‘parent population’ and it is desirable to make sure that each subgroup is fairly represented within the total sample. For example, suppose the population of a village is 10000 and on the basis of income variables, it is divisible into 10 groups, then a random sample for each of the subgroups will be taken repre­senting the income of the respective group.

The main advantage of the stratified sampling lies in the fact that it can be administered eas­ily and each stratum is represented in the sample (which may not be the case in random and purposive sampling), so that, if required, one can have separate estimates for stratum means. Stratified random sampling is widely used in agricultural, industrial and applied geo­graphical researches.

5. Multistage Sampling:

When the required sampling unit is reached through stages, it is called multistage sampling. For example, if 1000 families are to be selected for a land tenancy or socioeconomic survey of a meso or macro region, this can be done through multistage sampling, i.e., by first selecting a number of villages of the areal unit at random, and then selecting a number of families from each of the selected vil­lages.

This method of sampling is particularly useful for population covering large areas for which a list of individuals is not readily available or cannot be easily constructed. The method in general is cheaper but less accurate in comparison to the corresponding one of single stage sampling.

The statistical techniques of sampling described above are of great utility for researchers, dealing with the socioeconomic prob­lems of the society and also for those who are working in the fields of evolution of land forms, climatology, hydrosphere, etc.

The applica­tion of sampling techniques in geographical researches facilitates the task of researchers as they save time, efforts and cost appreciably and give fairly reliable results. In geography, in which graph theory, cor­relation, topology and transformation are at the emergent stage, sam­pling techniques have a significant role in the formulation of hy­pothesis, decision making, simulation and forecasting.